The　application　environment　of　a　farmland　Wireless　Sensor　Network（WSN）is　complex.The　factors　affecting　network　transmission　include　environmental　variables，crop　growth，etc.The　routing　protocol　is　an　important　link　in　the　network　data　collection　process.Therefore，research　activities　focused　on　energy　consumption　optimization　for　farmland　WSN　has　garnered　increased　attention　recently.Most　traditional　energy　consumption　optimization　routing　algorithms　are　designed　for　static　network　environments，which　are　difficult　to　apply　to　dynamic　farmland　monitoring　scenarios.Therefore，we　propose　a　routing　optimization　algorithm，namely，RD-PSO，based　on　improved　Particle　Swarm　Optimization（PSO）in　this　study.　Different　routing　transmission　paths　are　abstracted　as　particles，and　the　fitness　function　is　constructed　according　to　the　key　factors，such　as　farmland　network　energy　consumption，residual　energy，network　transmission　hops，and　link　quality，to　improve　the　environmental　adaptability　of　path　optimization.Furthermore，aiming　to　improve　the　low　iterative　efficiency　of　PSO　routing　during　random　initialization，a　reverse　detection　method　is　used　to　determine　the　initialization　topology　position　of　the　network　nodes，shorten　the　distance　between　the　initial　position　and　optimal　solution，and　improve　the　convergence　speed　of　the　algorithm.The　experimental　results　demonstrate　that　compared　with　ELMR，EEABR，and　MR-PSO　routing　algorithms，RD-PSO　attains　a　faster　convergence　speed　and　better　performance　in　network　life　cycle，energy　consumption　balance　effect，and　average　transmission　hops.These　developments　ensure　that　the　adaptability　of　our　routing　algorithm　is　superior　in　the　dynamic　environment　of　farmland　compared　with　the　existing　methods.　©　2022,　Editorial　Office　of　Computer　Engineering.　All　rights　reserved.